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Data analytics turns raw data into actionable insights. It includes a set of tools, techniques and processes used to identify trends and solve problems using data. Data analytics can influence how businesses operate, enhance decision-making, and promote corporate expansion.
Why is data analytics important?
Data analytics helps businesses gain clearer insight and a deeper understanding of their operations and services. Data analytics provide detailed insights into customer experience and issues. By moving the model beyond data to connect insights and actions, companies can create personalized customer experiences, build relevant digital products, improve operations, and increase employee productivity.
What is big data analytics?
Big data means large sets of diverse data, structured, unstructured and semi-structured, that are constantly generated at high speed and in large volumes. Big data is usually measured in terabytes or petabytes. One petabyte equals 1,000,000 gigabytes. To put it simply, one HD movie contains about 4GB of data. One petabyte equals 250,000 movies. Large data sets count anywhere from hundreds to thousands and millions of petabytes.
Big data analytics is the process of looking for patterns, trends, and linkages in huge data sets.
These complex analyzes require specific tools and techniques, computing power, and data storage that supports scaling.
How do big data analytics work?
Big data analytics involves five steps to analyze any large data set:
Data collection
Data storage
Data processing
Data cleaning
data analysisData collection
This process entails locating data sources and gathering information from them.
Data collection follows an ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) process.
ETL - Extract, Transform, and Load
In ETL, the generated data is first converted into a standard format and then the data is loaded into storage.
ELT - Extract, Load, Transform
In ELT, the data is first loaded into the volume and then converted into the required format.
Data storageThe data can be relocated to storage locations like data warehouses or cloud data warehouses, depending on how complicated the data is.Business intelligence tools can be accessed when needed.
Comparing data warehouses with data warehouses
A data warehouse is a database optimized for analyzing relational data received from transactional systems and business applications. Predefined data structure and data schema to optimize for speed in search and reporting. Data is cleaned, enriched, and transformed to serve as the "single source of truth" that users can trust. Examples of data include customer profiles and product information.
A data warehouse differs from a data warehouse in that it stores both structured data and unstructured data without performing any further processing. When collecting data, neither the data nor the data schema are defined; This means that you can store all your data without careful design and this is especially usefulif the data's intended purpose for the future is unclear. Examples of data include social media content, IoT device data, and non-relational data from mobile applications.
Organizations typically require both data warehouses and data warehouses to perform data analytics. AWS Lake Formation and Amazon Redshift can meet your data needs.
Data processing
When the data is in place, it must be transformed and organized to obtain accurate results from analytical queries. There are various options for processing data. The choice of method depends on the computational and analytical resources available to process the data.
Central processing
All processing takes place on a dedicated central server that hosts all data.
Distributed processing
The data is distributed and stored on different servers.
Batch or batch processing
Data accumulates over time and is processed in batches.
Real-time processing
Data is processed continuously, with computing tasks completed within seconds.
Data cleaning
Data cleaning involves revising and looking for any errors such as duplicates, inconsistencies, or wrong formats. It is also used to filter out any unwanted data for analytics purposes.
Data analysis
This is the step where raw data is transformed into actionable insights Four categories of data analytics exist:
1. Descriptive analytics
To comprehend what has occurred or is occurring in the data environment, data scientists examine data.This type of analysis is characterized by the visual display of data, such as pie charts, bar charts, line charts, tables, or descriptive narrative shapes.
2. Diagnostic analyses
Diagnostic analytics is the process of deep and detailed data analysis aimed at understanding why something is happening. This type of analysis is characterized by techniques such as drilling, data exploration, data mining, and correlations. In each of these techniques, several data operations and transformations are used to analyze raw data.
3. Predictive analytics
Predictive analytics uses historical data to make accurate predictions about future trends. This type of analytics features techniques such as machine learning, forecasting, pattern matching, and building predictive models. In each of these technologies, computers are trained to understand geometric causal links in the data.
4. Guideline analytics
Predictive analytics advances the use of predictive data. It not only predicts what is likely to happen, but also suggests an optimal response to this predicted outcome. Prescriptive analytics can analyze the potential effects of different choices and recommend the best course of action. This type of analysis is characterized by graphical analysis, simulation, complex event processing, neural networks, and recommendation engines.
What various data analytics methodologies are there?
Several computing technologies are used in data analytics. Here are some of the most common cases:
Natural language processing
The technology that enables computers to comprehend and respond to both spoken and written human language is known as natural language processing.Data analysts use this technology to process data such as dictation notes, voice commands, and chat messages.
Text mining
Data analysts use "text mining" to identify trends in textual data such as emails, tweets, research and blog posts. This technology can be used to categorize news content, customer reviews, and customer emails.
Sensor data analysis
Sensor data analysis is the process of examining data generated by various sensors. This type of analytics is used in predictive machine maintenance, shipment tracking, and other business processes in which machines generate data.
Anomaly analysis
Anomaly analysis, or anomaly detection, identifies data points and events that deviate from the rest of the data.
Can data analytics be automated?
Yes, data analysts can automate and improve processes. Data analytics process automation is the use of computer systems to perform analytical tasks with little or no human intervention. These mechanisms vary in complexity; From simple scripts or lines of code to data analysis tools that perform data modeling, feature discovery, and statistical analysis.
For example, a cybersecurity company might use automation to collect data from a huge amount of web activity and conduct further analysis, and then use visualization of the data to demonstrate results and support business decisions.
Can data analytics be outsourced?
Yes, companies can outsource data analysis assistance. Outsourcing data analytics allows management and the executive team to focus on other core business processes. Business analytics teams are experts in their field; They are aware of the latest data analytics techniques and are experts in managing data. This means they can perform data analyzes more efficiently, identify patterns, and successfully predict future trends. However, outsourcing may present business challenges due to the knowledge transfer and data confidentiality involved.
Data analytics improve business insights
Data analytics can be performed on datasets from different sources of customer data such as the following:
• Third-party customer surveys
• Customer purchase records
• Social media activity
• Cookies on your computer
• Website or app statistics
Analytics can reveal hidden information such as customer preferences, popular pages on a website, time customers spend browsing, customer reviews, and interaction with website forms. This enables companies to efficiently respond to customer needs and increase customer satisfaction.
Case study: How Nextdoor used data analytics to improve customer experience
Nextdoor is a neighborhood hub where reliable communication and exchange of useful information, goods and services take place. By harnessing the power of local community, Nextdoor helps people live happier, more effective lives. Nextdoor has used Amazon's analytics solutions to measure customer engagement and interaction and measure the effectiveness of their recommendations. Data analytics has empowered them to help customers build better connections and display more relevant content in real-time.
Data analytics leads to the implementation of effective marketing campaigns
Data analytics takes the guesswork out of marketing, product development, content creation, and customer service. Data analytics allows businesses to publish targeted content and improve its accuracy by analyzing data in real time. Data analytics also provides valuable insights into how marketing campaigns are implemented. Targeting, messaging and innovative advertising can be adjusted based on real-time analysis. Analytics can optimize your marketing to get more conversions and reduce ad waste.
Case Study: How Zynga used data analytics to power marketing campaigns
Zynga is one of the most successful mobile gaming companies in the world, offering premium games including Words With Friends, Zynga Poker, and FarmVille . More than a billion players around the world have installed these games. Zynga's revenue comes from in-app purchases, so it uses Amazon Kinesis Data Analytics to analyze players' real-time in-game actions to plan more effective in-game marketing campaigns.
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Data analytics increases operational efficiency
Data analytics helps companies streamline operations, reduce losses, and increase revenues. Predictive maintenance schedules, optimized rosters, and efficient supply chain management dramatically improve business performance.
Case study: How BT Group used business analytics to streamline operations
BT Group is the UK's leading telecommunications network, serving customers in 180 countries. BT Group's network support team used Amazon Kinesis Data Analytics to get a real-time view of calls made over the network within the UK. Network support engineers and troubleshooters use the system to identify, react to, and resolve network problems.
Case study: How Flutter used data analytics to speed up game operations
Flutter Entertainment is one of the largest online sports and gaming providers in the world. Its mission is to provide entertainment to more than 14 million customers in a safe, responsible and sustainable way. Over the past several years, Flutter has accumulated lots and lots of data from most source systems. Balancing volume with response time creates a constant challenge. Amazon Redshift helps Flutter scale to meet growing needs but provide a consistent end-user experience.
Data analytics lights the way during product development
Organizations use data analytics to identify and prioritize new features for product development. It can analyze customer requirements, deliver more features in less time, and launch new products faster.
GE Digital is a subsidiary of General Electric. GE Digital has many software products and services in several different sectors. One such product is Proficy Manufacturing Data Cloud. Amazon Redshift enables significantly improved data transformation and data response time and thus enables other benefits to customers.
Data analytics supports scaling of data operations
Data analytics offers automation in many data tasks such as migration, preparation, reporting, and integration. Data analytics eliminates manual inefficiencies and reduces the time and man-hours needed to complete data operations. This supports expansion and allows you to quickly expand into new ideas.
Case Study: How FactSet used data analytics to streamline customer integrations
FactSet's mission is to be the leading platform available for both content and analytics. Data transfer involves large processes, a variety of team members on the client side, and a group of individuals on the FactSet side. Anytime a problem occurred, it was difficult to pinpoint where in the traffic the error occurred. Amazon Redshift has helped streamline the process and enable FactSet customers to scale faster and bring in more data to meet their needs.
How is data analytics used in business?
Companies derive statistics, quantitative data, and information from multiple customer-facing and internal channels. But finding key insights requires careful analysis of a staggering amount of data. This is not easy. Study some examples of how data analytics and data science can add value to a company.
Data analytics improve business insights
Data analytics can be performed on datasets from different sources of customer data such as the following:
Customer surveys from third parties
Customer purchase records
Social media activity
Cookies on your computer
Website or application statistics
Analytics can reveal hidden information such as customer preferences, popular pages on a website, time customers spend browsing, customer reviews, and interaction with website forms. This enables companies to efficiently respond to customer needs and increase customer satisfaction.
Case study: How Nextdoor enhanced customer experience with data analytics
Nextdoor is the neighborhood hub where trusted communication and exchange of useful information, goods and services take place. And by harnessing the power of the local community, Nextdoor helps people live happier, more effective lives. Nextdoor used Amazon Analytics solutions to measure customer engagement and the effectiveness of their recommendations. Data analytics has empowered them to help customers build better connections and display more relevant content in real-time.
Data analytics leads to the implementation of effective marketing campaigns
Data analytics takes the guesswork out of marketing, product development, content creation, and customer service. Data analytics allows businesses to publish targeted content and improve its accuracy by analyzing data in real time. Data analytics also provides valuable insights into how marketing campaigns are implemented. Targeting, messaging and innovative advertising can be adjusted based on real-time analysis. Analytics can optimize your marketing to get more conversions and reduce ad waste.
Case Study: How Zynga used data analytics to power marketing campaigns
Zynga is one of the most successful mobile gaming companies in the world, offering premium games including Words With Friends , Zynga Poker , and FarmVille . More than a billion players around the world have installed these games. Zynga's revenue comes from in-app purchases, so it uses Amazon Kinesis Data Analytics to analyze players' real-time in-game actions to plan more effective in-game marketing campaigns.
Data analytics increases operational efficiency
Data analytics helps companies streamline operations, reduce losses, and increase revenues. Predictive maintenance schedules, optimized rosters, and efficient supply chain management dramatically improve business performance.
Case study: How BT Group used business analytics to streamline operations
BT Group is the UK's leading telecommunications network, serving customers in 180 countries. BT Group's network support team used Amazon Kinesis Data Analytics to get a real-time view of calls made over the network within the UK. Network support engineers and troubleshooters use the system to identify, react to, and resolve network problems.
Case study: How Flutter used data analytics to speed up game operations
Flutter Entertainment is one of the largest providers of online sports and games in the world. Its mission is to provide entertainment to more than 14 million customers in a safe, responsible and sustainable way. Over the past several years, Flutter has accumulated lots and lots of data from most source systems. Balancing volume with response time creates a constant challenge. Amazon Redshift helps Flutter scale to meet growing needs but provide a consistent end-user experience.
Data analytics lights the way during product development
Data analytics is used by businesses to identify and rank new features for product development. It can analyze customer requirements, deliver more features in less time, and launch new products faster.
Case Study: How GE accelerated product delivery with data analytics
GE Digital is a subsidiary of General Electric. GE Digital owns many software products and services in several different sectors. One such product is Proficy Manufacturing Data Cloud.
Amazon Redshift enables significantly improved data transformation and data response time and thus enables other benefits to customers.
Data analytics supports scaling of data operations
Data analytics offers automation in many data tasks such as migration, preparation, reporting, and integration. Data analytics eliminates manual inefficiencies and reduces the time and man-hours needed to complete data operations. This supports expansion and allows you to quickly expand into new ideas.
Case Study: How FactSet used data analytics to streamline customer integrations
FactSet's mission is to be the leading platform available for both content and analytics. Data transfer involves large processes, a variety of team members on the client side, and a group of individuals on the FactSet side. Anytime a problem occurred, it was difficult to pinpoint where in the traffic the error occurred. Amazon Redshift has helped simplify the process and enable FactSet customers to scale faster and bring in more data to meet their needs.
How does AWS help with data analytics?
AWS provides data analytics services that are comprehensive, secure, scalable, and affordable. AWS Analytics Services address all data analytics needs, enabling organizations of all sizes and industries to reinvent their businesses with data. AWS offers purpose-built services that provide the best pricing performance: data movement, data storage, data warehouses, big data analytics, machine learning, and everything related to that.
Amazon Kinesis Data Analytics is the simplified way to move and analyze streaming data in real-time using Apache Flink. It provides built-in functionality to filter, aggregate, and transform stream data for advanced analytics.
Amazon Redshift lets you query exabytes of structured and semi-structured data and consolidate them into your data warehouse, operational database, and data lake.
Amazon QuickSight is a business intelligence (BI) service built for the cloud, powered by machine learning (ML), scalable, serverless, and embeddable. By using QuickSight, you can easily create and deploy business intelligence (BI) dashboards with machine learning-powered insights.
Amazon OpenSearch Service makes it easy to perform interactive log analyses, real-time application monitoring, website search, and more.
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